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Title:
Distortion invariant pattern recognition using neural network based shifted phase-encoded joint transform correlation
Authors:
Islam, Mohammed Nazrul; Islam, Md. Habibul; Asari, K. Vijayan; Karim, Mohammad A.; Alam, Mohammad S.
Affiliation:
AA(Old Dominion Univ. (USA)), AB(Bangladesh Univ. of Engineering and Technology (Bangladesh)), AC(Old Dominion Univ. (USA)), AD(Old Dominion Univ. (USA)), AE(Univ. of South Alabama (USA))
Publication:
Optical Pattern Recognition XX. Edited by Casasent, David P.; Chao, Tien-Hsin. Proceedings of the SPIE, Volume 7340 (2009)., pp. 734009-734009-7 (2009). (SPIE Homepage)
Publication Date:
04/2009
Origin:
AIP
Abstract Copyright:
(c) 2009: American Institute of Physics
DOI:
10.1117/12.819530
Bibliographic Code:
2009SPIE.7340E...7I

Abstract

An optoelectronic neural network based detection technique is proposed for multi-class distortion-invariant pattern recognition. The neural network is utilized in the training stage for a sequence of multi-class binary and gray level images for supervised learning using shifted phase-encoded joint transform correlator with fringe adjusted filter in the hidden layer to create composite images that are invariant to distortion. Simulation results show that the proposed technique is efficient in recognizing targets in variable environmental conditions.
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Database: Astronomy
Physics
arXiv e-prints